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8/17/2014 Information Processing: Neural Networks and Deep Learning http://infoproc.blogspot.nl/2014/08/neural-networks-and-deep-learning.html 1/12 More Next Blog» Create Blog Sign In Information Processing Pessimism of the Intellect, Optimism of the Will Archive Favorite posts Twitter: @steve_hsu Stephen Hsu Google+ Profile Saturday, August 16, 2014 Neural Networks and Deep Learning One of the SCI FOO sessions I enjoyed the most this year was a discussion of deep learning by AI researcher Juergen Schmidhuber. For an overview of recent progress, see this recent paper. Also of interest: Michael Nielsen's pedagogical book project. An application which especially caught my attention is described by Schmidhuber here: Many traditional methods of Evolutionary Computation [15-19] can evolve problem solvers with hundreds of parameters, but not millions. Ours can [1,2], by greatly reducing the search space through evolving compact, compressed descriptions [3-8] of huge solvers. For example, a Recurrent Neural Network [34-36] with over a million synapses or weights learned (without a

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Page 1: Information Processing_ Neural Networks and Deep Learning

8/17/2014 Information Processing: Neural Networks and Deep Learning

http://infoproc.blogspot.nl/2014/08/neural-networks-and-deep-learning.html 1/12

More Next Blog» Create Blog Sign In

Information ProcessingPessimism of the Intellect, Optimism of the Will Archive Favorite posts Twitter: @steve_hsu

Stephen Hsu

Google+ Profile

Saturday, August 16, 2014

Neural Networks and Deep Learning

One of the SCI FOO sessions I enjoyed the most this year was adiscussion of deep learning by AI researcher JuergenSchmidhuber. For an overview of recent progress, see this recentpaper. Also of interest: Michael Nielsen's pedagogical bookproject.

An application which especially caught my attention is describedby Schmidhuber here:

Many traditional methods of Evolutionary Computation[15-19] can evolve problem solvers with hundreds ofparameters, but not millions. Ours can [1,2], by greatlyreducing the search space through evolving compact,compressed descriptions [3-8] of huge solvers. Forexample, a Recurrent Neural Network [34-36] withover a million synapses or weights learned (without a

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teacher) to drive a simulated car based on a high-dimensional video-like visual input stream.

More details here. They trained a deep neural net to drive a carusing visual input (pixels from the driver's perspective, generatedby a video game); output consists of steering orientation andaccelerator/brake activation. There was no hard coded structurecorresponding to physics -- the neural net optimized a utilityfunction primarily defined by time between crashes. It learnedhow to drive the car around the track after less than 10k trainingsessions.

For some earlier discussion of deep neural nets and theirapplication to language translation, see here. Schmidhuber hasalso worked on Solomonoff universal induction.

These TED videos give you some flavor of Schmidhuber's senseof humor :-) Apparently his younger brother (mentioned in thefirst video) has transitioned from theoretical physics to algorithmicfinance. Schmidhuber on China.

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Posted by Stephen Hsu at 7:28 PM Labels: ai, brainpower, computing, machine learning, scifoo,singularity

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Blog Archive▼ 2014 (116)

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